A Comprehensive Framework for AI-Enhanced Data Integration in Business Process Mining

Abstract

In recent years, business process mining has emerged as a powerful tool for extracting actionable insights from event logs, providing organizations with the ability to analyze, monitor, and optimize business processes. However, a critical challenge in process mining lies in the integration of diverse data sources that often lack harmonization, suffer from inconsistencies, and have varying levels of data quality. The increasing complexity of data environments and the emergence of new data streams necessitate more sophisticated methods for enhancing the integration process, ensuring that data is unified, accurate, and actionable. This paper presents a comprehensive framework for AI-enhanced data integration in business process mining, aiming to address these challenges and provide a robust solution for integrating heterogeneous data sources. The proposed framework leverages cutting-edge AI techniques to improve data harmonization, enhance data quality, and ensure the accuracy of integrated data, ultimately enabling organizations to derive reliable business insights that can guide process optimization and decision-making.

The primary contribution of this research is the development of an AI-driven integration model that incorporates various machine learning, natural language processing (NLP), and deep learning techniques. These AI methods are specifically tailored to process the complexities of different data sources, including structured and unstructured data, from enterprise systems such as ERP, CRM, and IoT devices. By utilizing advanced data harmonization algorithms, the framework can align disparate data formats, ensuring that data is coherent and consistent across systems. Furthermore, the integration framework employs AI techniques to detect and correct data anomalies, filling gaps in missing data and reconciling conflicting information. This improves the overall data quality, thereby enhancing the reliability of process mining outcomes.

A key aspect of the framework is the incorporation of real-time data processing capabilities. With the rise of real-time analytics, organizations increasingly require data integration systems that can handle continuous data streams, such as transactional data or sensor data, in a seamless and efficient manner. The framework facilitates real-time integration, allowing organizations to conduct process mining and process optimization on the most up-to-date data, which is essential for agile decision-making and operational efficiency.

To demonstrate the effectiveness of the proposed framework, this paper presents several case studies across diverse industries, including manufacturing, healthcare, and finance. These case studies highlight how the AI-enhanced data integration framework can be applied in real-world scenarios, offering substantial improvements in business process mining applications. In the manufacturing sector, for example, the framework enables better integration of production data from multiple sources, leading to more accurate process discovery and bottleneck detection. In healthcare, the framework integrates patient data from various clinical systems, improving the accuracy of process mining in clinical workflows, while in finance, it enhances the integration of transactional and audit data for more precise risk analysis and fraud detection.

The paper also discusses the technical challenges involved in AI-enhanced data integration, such as the complexity of processing large-scale datasets, handling diverse data formats, and maintaining data privacy and security. It outlines potential solutions for these challenges, including the use of distributed computing, data encryption, and privacy-preserving techniques such as federated learning. Additionally, the paper explores the limitations of current AI methods in business process mining, proposing directions for future research in areas such as explainable AI (XAI) for process discovery, and the integration of advanced anomaly detection techniques to further improve data quality and harmonization.

Furthermore, the framework presented in this paper has significant implications for future trends in business process mining. As organizations continue to adopt AI-driven technologies, the demand for more advanced and integrated data analytics tools will only increase. The proposed framework not only addresses the current limitations of process mining but also positions itself as a scalable solution for future developments, ensuring that businesses can stay ahead in an increasingly data-driven environment.